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Record W4392907919 · doi:10.32920/25412614

An Architecture of Shifting Mores: Transforming the Modern Downtown

2024· preprint· en· W4392907919 on OpenAlexaboutno aff
Konner S. Mitchener

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsMoresArchitectureAestheticsSociologyDowntownModernization theoryEnvironmental ethicsPolitical scienceGeographyArtLawVisual artsArchaeology

Abstract

fetched live from OpenAlex

Since its inception, modernism has represented an embrace of changing socioeconomic conditions and has been characterized as a medium for enacting social change. Modernism is both shaped by and contributes to the moulding of changing social mores. The built fabric acts as an indicator of cultural values but is also acted upon by its inhabitants to reflect shifting social norms. An architecture reflective of shifting mores embraces this duality and uses it to instill a sense of optimism for the future. This thesis contends that modernism embodies a cycle of ever-evolving values and cultural shifts. As collective opinions change and socioeconomic conditions are transformed, it is essential that we demand new uses and meanings from our built environment. An architecture of shifting mores diverges from traditional modernist thinking. We can no longer wipe the slate clean to serve our shifting needs. This thesis is sited in London, Ontario’s Downtown Heritage Conservation District where ideas of pragmatism, hybridity, innovation, and shifting mores were tested on a city block, resulting in the proposed design framework. This thesis responds to the conditions of modernization that have resulted in the city as we know it. The framework being proposed puts cultural heritage at the forefront of city building while finding creative, new uses for underutilized infrastructure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.264
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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